Technical Challenges Along the Way to HIE Sustainability

Egor Kobelev of DataArt contributes a byline article to Electronic Health Reporter on the technical challenges of health information exchanges and analyzes some of the most effective approaches for solving them.

“There are a lot of organizational and technical challenges health information exchanges (HIEs) struggle with while trying to deploy and maintain their platforms. One of the most complex organizational and administrative challenges is to achieve sustainability. While that is often an ultimate goal for HIEs, there is a huge amount of smaller technical challenges to meet, and the way those challenges are responded to often makes a difference for future HIE sustainability.

One of those typical tasks in the industry is a patient look up and mapping. There is a well-known issue when it comes to any sort of health data integration – the lack of a global unique patient identifier. Thousands of existing healthcare providers and payers use their own internal identifiers and there is no easy way to establish a relation between these. Social Security Numbers or similar national identifiers, while useful in some of scenarios, are not suitable for the purposes of healthcare record identification, primarily because of the risks of HIPAA rules violation.

One of the most efficient approaches combines probabilistic machine matching, IDs mapping and the ability to fallback to a manual review when/if necessary. Let’s say that healthcare organization A receives a message with patient health information from organization B. Both A and B maintain their own unique identification systems, which are incompatible. So A received patient health information, which, as a bare minimum, consists of name, date of birth, gender, and organization B patient ID (B.ID for simplicity). Apparently the first step for A to take to match the patient is to go with probabilistic matching against its own patient database based on demographic information. Date of birth and gender are usually straightforward, name could be tricky, because of the complications caused by middle names, titles, suffixes, prefixes, etc. Those are often stored in unstructured formats, written differently and with no strict order. However, the majority of titles and prefixes are manageable by an exhaustive search against a dictionary, so that will leave just several combinations of first, middle and last names, handled either by strict equality or something like SoundEx, which works pretty well even in its canonical implementation…”

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